Audio AI on the Edge: A Primer for Device Makers

AI Has Changed Audio, But Not Audio Hardware (Yet)

- By Music.AI

AI has already transformed the way we process sound.

Features such as stem separation, automatic transcription, and voice cloning have all helped speed up workflows for many audio professionals. Until recently, however, AI-powered audio processing has primarily occurred in the cloud, often leaving embedded engineers on the sidelines for some of the most promising advances.

The good news is that with recent innovations in hardware, models, and AI deployments, this is starting to change.

HERE ARE TWO OF THE BIGGEST REASONS WHY:

AI Accelerators—Advances in dedicated AI hardware, including embedded GPUs like those in NVIDIA's Jetson series, neural processing units (NPUs) such as Google’s Coral Edge TPU, and SoCs with builtin AI cores such as Qualcomm Hexagon series, make it far more efficient to run machine-learning models on resource-constrained edge devices.

Model Compression & Quantization—Techniques such as 8-bit quantization, pruning, and knowledge distillation reduce model complexity and computational demands, making neural networks efficient enough to run on resource-constrained edge devices.

FIGURE 1 AI accelerators bring efficient machine-learning models to the edge.

WHY THE EDGE IS THE FUTURE OF AUDIO AI

While many of the current solutions are very effective, cloud-based AI audio features also come with major limitations:

• Privacy: Data privacy and security can be particularly challenging when services are provided over the internet.

• Latency: Cloud-based solutions often come with increased latency, which can make certain use cases untenable.